Sorting materials using pattern recognition, such as upgrading nickel laterite ores through electromagnetic sensor-based methods

ABSTRACT

A system and method of sorting mineral streams, for example laterite mineral ores, into appropriately classified valuable and waste streams for maximum recovery of value from the mineral stream, e.g., a stream of minerals includes receiving response data indicating reflected, absorbed or backscattered energy from a mineral sample exposed to a sensor, where the mineral sample is irradiated with electromagnetic energy. The system determines spectral characteristics of the mineral sample by performing spectral analysis on the response data of the mineral sample and identifies a composition of the mineral sample by comparing the spectral characteristics of the mineral sample to previously developed spectral characteristics of samples of known composition. The system then generates a sort decision for the mineral sample based on the comparison, where the sort decision is used in diverting the mineral sample to a desired destination e.g. pyrometallurgical treatment stages, or to a waste stream.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. 119(e) of U.S.Provisional Application No. 61/640,749, filed on May 1, 2012, entitled“A Method For Upgrading Nickel Laterite Ores Through ElectromagneticSensor-Based Methods,” which is hereby incorporated by reference for allpurposes in its entirety. This application is a continuation-in-part ofU.S. application Ser. No. 13/538,931, filed Jun. 29, 2012, entitled“Extracting Mined Ore, Minerals or Other Materials Using Sensor-BasedSorting,” which in turn claims the benefit of U.S. ProvisionalApplication No. 61/502,772, filed on Jun. 29, 2011, entitled “Method forthe Pre-Concentration of Mineral Ores” and U.S. Provisional ApplicationNo. 61/502,760, filed on Jun. 29, 2011, entitled “High FrequencyElectromagnetic Spectrometer,” all of which are hereby incorporated byreference for all purposes in their entireties.

BACKGROUND

Material extracted from the earth may be processed using various miningprocesses. Using various techniques, after materials are mined from theground, they are typically blended to achieve as much as possible ahomogeneous condition. Thereafter, those portions of the blendedmaterial that have no beneficial use or value are typically separated orextracted from the portions of the material that have beneficial use orvalue by various conventional means.

For example, rock material may be mined using explosives, excavated andthen transported to crushers that crush the rock material into smallergrain size. After crushing, the rock material may be further groundfiner in grinding mills. The process may also include a vibrating screenthat classifies the crushed or ground material into desired grain sizes.Next, valuable minerals may be concentrated by removing unnecessarysubstances from the excavated rock material. The separation process mayinclude leaching, flotation, gravity methods and magnetic separation, orconcentration by pyrometallurgical methods.

After separating the most valuable fragments, metal may be extractedfrom the mineral. Common extraction methods include pyrometallurgy (ametal production method employing high temperatures), alternatelyhydrometallurgy (producing metal by leaching the raw material andprecipitating the pure metal from the solution) and alternatelyelectrometallurgy (a metal production method applying electricity).

BRIEF DESCRIPTION OF DRAWINGS

Embodiments of the present disclosure will be described and explainedthrough the use of the accompanying drawings in which:

FIG. 1 illustrates an example of an arrangement for a sorting machine;

FIG. 2 illustrates an example of a control system with embedded patternrecognition and discrimination algorithms;

FIG. 3 illustrates an example of an arrangement of a sorting system;

FIG. 4 is a flow chart having an example set of instructions foridentifying mineral composition; and

FIG. 5 an example of a computer system with which one or moreembodiments of the present disclosure may be utilized.

The drawings have not necessarily been drawn to scale. For example, thedimensions of some of the elements in the figures may be expanded orreduced to help improve the understanding of the embodiments of thepresent invention. Similarly, some components and/or operations may beseparated into different blocks or combined into a single block for thepurposes of discussion of some of the embodiments of the presentinvention. Moreover, while the disclosure is amenable to variousmodifications and alternative forms, specific embodiments have beenshown by way of example in the drawings and are described in detailbelow. The intention, however, is not to limit the disclosure to theparticular embodiments described. On the contrary, the disclosure isintended to cover all modifications, equivalents, and alternativesfalling within the scope of the disclosure.

DETAILED DESCRIPTION

In the field of mineral extraction and beneficiation, several methodsexist for the extraction and beneficiation of lateritic nickel ores toferronickel. Lateritic deposits are so named in cases where silica hasbeen selectively leached from a nickel containing ultramaficmagnesium-atuminosilicate deposit, selectively concentrating the nickelin heterogeneous proto-horizontal layers. Mining by mechanised open pitmethods is common, however two principal beneficiation methods are useddepending on the specific composition of the ore.

Iron-rich, predominantly limonitic ores are mined, blended, and treatedhydrometallurgically, either by acid heap leach at atmospheric pressure,or by pressurized acid leach methods. Ferronickel is produced from theleachate by precipitation and electrorefining. Silica-rich predominantlysaprolitic ores are treated pyrometallurgically firstly by calcining thecrushed, mined and blended saprolite, and then smelting of the calcine,by either AC or DC methods. Lateritic deposits are seldom solely of onetype or the other and generally contain significant quantities of bothlimonite and saprolite as well as intermediate or transition material,plus remnant silica basement material along with other diluentspresented in a highly heterogeneous manner.

Each method is therefore compromised in the presence of too much of theother lithology (i.e., too much saprolite in the limonite whenhydrometallurgical routes are selected, and too much limonite in thesaprolite when pyrometallurgical methods are selected). Both treatmentmethods are compromised in the case of unilaterally low grades ofnickel.

A novel solution to the common challenge of economic processing oflaterites is described herein. It is possible to determine the variablechemical composition of unblended mineral samples or streams by exposingthe mineral sample or stream to electromagnetic radiation and measuringa signal produced therefrom, such as an absorption, reflectance orCompton backscatter response. A machine comprising arrays ofsource-detector-type mineral sensors, coupled to high-speed, digitalsignal processing software incorporating rapid pattern recognitionalgorithms scans the ore stream in real-time and interprets the chemicalcomposition of the ore. An array of physical diverters connected to thesensor array via a high-speed, real-time machine control layer areactuated to deflect the mineral sample or stream when the mineralcomposition as measured by the sensor array reaches a certainpre-determined value.

Embodiments of the present invention applied in the form of arrays ofthe described machines, sensibly arranged in a logical process sequence,can process large quantities of unblended laterite material minedunselectively at high throughput rates into streams of materialsimultaneously curated either for hydrometallurgical-optimal treatment,pyrometallurgical-optimal treatment, or as a waste product for disposalback into the mining void. Accordingly, in some embodiments, multipleeconomic streams from one deposit may simultaneously be produced usingsensor-based methods.

Embodiments of the present invention described herein depart fromconventional practice whereby in some described embodiments, therecovered value of mineral ores is maximized by preserving the naturalheterogeneity of the mineral ore as much as possible by not blending themineral ore prior to introducing it into a sorting system trained torecognise distinct species within the heterogeneous material. In someembodiments, a sorting system may then simultaneously classify themineral ore into several fractions of arbitrary but variant value forprocessing in separate treatment streams (including a waste streamdesignated for disposal), thereby maximizing the recovered value of themineral ore.

While multiple embodiments are disclosed, still other embodiments of thepresent disclosure will become apparent to those skilled in the art fromthe following detailed description, which shows and describesillustrative embodiments. As will be realized, embodiments of thepresent invention are capable of modifications in various aspects, allwithout departing from the scope of the present disclosure. Accordingly,the drawings and detailed description are to be regarded as illustrativein nature and not restrictive.

The terminology used below is to be interpreted in its broadestreasonable manner, even though it is being used in conjunction with adetailed description of certain specific examples of the invention.Indeed, certain terms may even be emphasized below; however, anyterminology intended to be interpreted in any restricted manner will beovertly and specifically defined as such in this Detailed Descriptionsection.

FIG. 1 illustrates an example of an arrangement for a sorting machine.The sorting machine may include a feed mechanism, an electromagneticsource/detector array, and a control enclosure. The control enclosuremay include an analog to digital signal conversion and signal analysissystem, and a diverter array system connected to the control enclosure.

A sorter 100 may include a Teflon-lined, steep-sided feed bin suitablefor accepting clay or rocky material feed 10, delivering material to asorting conveyor 20 driven by variable speed motor 30. Material presenton the sorting conveyor 20 may be detected by a scanning laser 40 whichactivates an electromagnetic energy source array 50. Consequentelectromagnetic radiation absorption, reflectance or backscatter fromthe material present in the source energy field may be detected by adetector array 60. Analogue signals from the detector array may then beconverted by analogue to digital signal converter 70 to digital form andbe passed to a digital signal processing stage 80 where Fourier Analysisis performed to generate a discrete power spectrum analyzed by frequencyor wavelength.

Discrete power spectrum data thus generated may then be compared usingpattern recognition algorithms to determine the mineral content. Resultsof the pattern recognition algorithm may then be compared topre-determined results in an embedded industrial computer 90. A divertercomprising relay/solenoid 110, actuator 120 and gate 130 may becontrolled by the embedded industrial computer 90 via the programmablelogic controller 100. Material with a recognized chemical compositionabove a certain pre-set value may be diverted to a product chute 140.Material with a recognized chemical composition below a certain pre-setvalue may be diverted to a waste chute 150.

FIG. 2 illustrates an example of a control system with embedded patternrecognition and discrimination algorithms. The control system mayinclude an analogue to digital conversion stage, a digital signalprocessing stage, a pattern recognition stage, comparator stage and adiverter array control stage.

Signals of arbitrary waveform, wavelength and frequency from a detectorarray 200 may be converted by analog to digital signal converter 210.Digital signals from the converter 210 may be passed to a FourierAnalysis stage where spectral data of amplitude/frequency oramplitude/wavelength format may be generated by Fast Fourier Transformimplemented on a field programmable gate array (FPGA) 220 or othersuitable device (e.g. a digital signal processor (DSP), applicationspecific IC (ASIC), microcontroller, etc.). Arbitrary power spectragenerated 230 in the Fourier Analysis stage 220 may be compared topreviously determined and known spectra 260. Spectra of desired materialmay be recognized by pattern recognition algorithm 240 running on anembedded computer 250. Recognition of desired material may result in“accept” instructions being passed from the embedded computer 250 to thediverter array 280 via a programmable logic controller 270 or othersuitable device (e.g. FPGA, DSP, ASIC, microcontroller, etc.).Recognition of undesired material may result in “reject” instructionsbeing passed to a diverter array 280. The equivalency between likecomponents in FIGS. 2 and 1 are evident—detector array 200 of FIG. 2 isequivalent to detector array 60 in FIG. 1; signal converter 210 tosignal converter 70; embedded computer 250 to computer 90; diverterarray 280 to diverter gate 130; PLC 270 to PLC 100 and so on.

Referring now to the pattern recognition algorithm in more detail, theconcepts of recognition and identification as used in biometric securityare introduced. Automated digital signal analysis is conventionallyapplied for pattern recognition using an exact matched, or identified,signal. In spectrum matching, both wavelength and amplitude, orfrequency and amplitude of an arbitrary power spectrum are to bematched. Traditional pattern matching requires comparison of everyinbound spectrum to the sample spectrum to achieve an exact match and iscomputationally very intensive and time consuming and therefore notpractical in high-speed mineral recognition applications. Recognition ishereby differentiated from identification, or matching, for the purposeof the present system. As used in biometric security, for instance,recognition is the verification of a claim of identity, whileidentification is the determination of identity. These scenarios looselycorrespond to the use of sensor telemetry for classification (e.g.,sorting applications in the field) and characterization (e.g.,analytical operations in the laboratory). To build further intuition,the biometric identification/recognition scenario will be furtherelucidated:

Identification

In the laboratory, a sample might be subjected to, for example, an X-rayFluorescence sensor for analytic purposes. In the mining practice ofinterest, a spectral pattern is created in the lab using analyticalprocedures (i.e., samples from the deposit of interest are characterizedor identified using analytical procedures in the lab). This is to saythat the objective of the sampling is to yield the most accurate andprecise result: a sensor-based assay. In this way the identity of amineral sample as determined by sensor-based techniques is a prioridetermined. This template is programmed into field units so that resultsfrom new samples can be compared to it in quasi-real time.

The biometric analogy might go as follows: You are returning to yourhome country at one of its major international airports and have theoption of using a kiosk equipped with an iris scanner. You simplyapproach the kiosk and present only your eye for examination by thescanner. The kiosk reads your iris and prints out a receipt with yourname on it for you to present to a customs agent. The kiosk has clearlysearched for a closest match to the sample you just provided, from adatabase of templates. You have been identified by the kiosk. Leavingaside the question of whether or not this is good security practice, itis clear that the kiosk is programmed to minimize the possibility ofidentity fraud (i.e., the incidence of false acceptance).

Recognition

In the field, samples are to be analyzed quickly—in quasi-real time—inorder to produce economically viable results. There is neither time nor,as it turns out, need for exactitude in matching. A sample is to simplymatch the a priori pattern within a pre-determined tolerance; it is thenrecognized as a positive instance, or else it is classified as anegative instance.

It is therefore necessary only to recognize the emerging spectralpattern, based on the a priori identification described above, in timeto make a classification decision.

The biometric analogy might go as follows: You are returning to yourhome country at one of its major international airports and have theoption of using a kiosk equipped with an iris scanner. You approach thekiosk and present your passport, thereby making an identity claim. Youthen present your eye for examination by the scanner. The kiosk readsyour iris and compares the sample to a stored template (derived,perhaps, from information encrypted in your passport). Identity has beenrapidly confirmed by recognition of the subject based on a prioriknowledge of the subject content. This is analogous to the patternrecognition algorithm deployed in various embodiments of the presentinvention.

The advanced pattern recognition methodology deployed involves patternlearning (or classification) of absorbed, reflected or backscatteredenergy from the irradiation of previously characterized mineral samplesand pattern recognition comprising fuzzy analysis and resource-boundedmatching of absorption, reflectance or backscattered spectra from newlyirradiated mineral samples through a trained conditional random field(CRF) algorithm. The algorithms that match of absorption, reflectance orbackscattered spectra may be resource-bounded, meaning that energyphysics determines when measurement of a sample is complete.

Referring now to the CRF algorithm, CRF involves the “training” of therandom field on known spectra, as well as the use of the random fieldunder resource bounded conditions to rapidly recognize new spectrasimilar to the “trained” spectrum. In contrast to an ordinary matchingalgorithm which predicts a result for a single sample without regard to“neighboring” samples, the CRF algorithm deployed predicts a likelysequence of results for sequences of input samples analysed. Let X be anarray observed spectral measurements with Y a corresponding array ofrandom output spectra. Let

S=[V,E]  (1)

be a set of spectra such that

Y=(Yv)_(veV)  (2)

so that Y is indexed by the vertices of S. Then (X,Y) is a conditionalrandom field when the random variables Y_(v), conditioned on X, obey theMarkov property

p(Yv|X,Yw,w≠v)=p(Tv|X,Yw,w˜v)  (3)

where w˜v means that w and v are neighbours or near neighbours in S. Theconditional distribution

p(Y|X)  (4)

is then modeled. Learning parameters θ are then obtained by maximumlikelihood learning for

p(Yi|Xi:θ)  (5)

where all nodes have exponential family distributions and optimizationis convex and can be solved by, e.g., gradient-descent algorithms. Thelearning, or characterization, phase involves identifying commoncharacteristic spectra generated from a series of samples by repeatedexposure of the spectral analyzer to the samples. These characteristicfeatures may then be used for efficient and rapid spectrum recognitionfor new samples with similar spectra.

FIG. 2 references therefore a pattern recognition algorithm of theconditional random field type, using back-propagation when in thetraining mode to define matching coefficients e for the conditionalrandom field, which additionally incorporates pseudo-random sampling,and boundary detection comprising confirmation of the spectral upper andlower bounds. The system is trained to recognize the presence of a rangeof typical mineral constituents in a matrix such as iron, aluminium,silica and magnesium present in a sample which is moving with referenceto the sensor, calculate the specific and total concentration of eachelement in the sample and compare it to the pre-defined spectrum ofknown material obtained during the “training” phase of the algorithmdevelopment.

Other pattern recognition algorithms such as inter alia brute-force,nearest-neighbour, peak matching etc. may be used. As such, embodimentsof the present invention are not limited to the particular algorithmdescribed. For example, the peak frequencies from a few samples withcertain amplitudes may be identified, and then each sample may beanalyzed for peaks near those frequencies and above a certain amplitude.

FIG. 3 illustrates an example of an arrangement of a sorting system inan open pit mining application. Embodiments depicted in FIG. 3 may beused, for example to classify a pyrometallurgical process feed, ahydrometallurgical process feed and a waste product simultaneously fromthe same deposit. Typical bulk open pit mining equipment deliversunblended mineral feed to an ore sorting facility comprising arrays ofelectromagnetic sorting machines described. Saprolitic material producedby the sorting facility is delivered to pyrometallurgical plant 480.Limonitic material simultaneously recovered by the sorting facility isdelivered to hydrometallurgical plant 550. Waste material simultaneouslyrecovered by the sorting facility is delivered to waste piles 470, 540for repatriation to the open pit.

Unblended laterite material 310 from the open pit may be delivered bytruck 320 to coarse separator 330. Fine fractions from separator 330underflow may be passed to fine sorter feed bin 340 where material maybe held prior to delivery to sorting conveyor 350. Material travellingon the sorting conveyor 350 may be scanned by an array ofelectromagnetic sensors 360. Results from the electromagnetic sensors360 may be passed to controller 370 which compares the sensor results topre-set values and may instruct the diverter 380 to divert the materialaccording to its chemical content. High iron limonitic material may bediverted to limonite sorter 490. High silica saprolitic material may bediverted to saprolite sorter feed bin 560.

High iron limonitic material from the sorting conveyor 350 may be passedto the limonite sorter feed bin 490 where material is held prior todelivery to sorting conveyor 500. Material traveling on the sortingconveyor 500 may be scanned by an array of electromagnetic sensors 510.Results from the electromagnetic sensors 510 may be passed to controller520 which compares the sensor results to pre-set values and instructsdiverter 530 to divert the material according to its chemical content.Material not suitable for treatment is diverted to the waste pile 540.Limonitic material suitable for treatment is passed via the limoniteproduct conveyor to the hydrometallurgical facility 550.

Similarly high silica saprolitic material from the sorting conveyor 350may be passed to saprolite sorter feed bin 560 where material may beheld prior to delivery to sorting conveyor 570. Material travelling onthe sorting conveyor may be scanned by an array of electromagneticsensors 580. Results from the electromagnetic sensors 580 may be passedto the controller 590 which compares the sensor results to pre-setvalues and instructs the diverter 600 to divert the material accordingto its chemical content. Material not suitable for treatment is divertedto the waste pile 540. Saprolitic material suitable for treatment ispassed via the saprolite product conveyor 460 to pyrometallurgicalfacility 480.

Coarse fractions from the separator 330 overflow may be passed to coarsesorter feed bin 410 where material may be held prior to delivery to thesorting conveyor. Material traveling on sorting conveyor 420 may scannedby an array of electromagnetic sensors 430. Results from the array ofelectromagnetic sensors 430 may be passed to controller 440 whichcompares the sensor results to pre-set values and instructs the diverterarray 450 to divert the material according to its chemical content. Highnickel saprolitic material may be diverted to saprolite product conveyor460. Low nickel, high iron and high silica material may be diverted tothe waste pile 470. Note that some elements may be combined together,such as a single controller that performs comparisons and instructsdiverters.

FIG. 4 is a flowchart having an example set of instructions fordetermining mineral content. The operations can be performed by variouscomponents such as processors, controllers, and/or other components. Inreceiving operation 410, response data from a mineral sample isreceived. The response data may be detected by a scanner that detectsthe response of the mineral sample to electromagnetic radiation (i.e.,reflected or absorbed energy). An analog to digital converter maydigitize the response data.

In determining operation 420, the spectral characteristics of themineral sample may be determined. A spectral analysis may be performedon the response data to determine characteristics of the mineral sample.Characteristics may include frequency, wavelength, and/or amplitude. Insome embodiments, characteristics include other user-definedcharacteristics.

In identifying operation 430, a composition of the mineral sample isidentified by comparing the characteristics of the mineral sample tocharacteristics of known mineral samples. Pattern matching algorithmsmay be used in identifying the composition.

In assigning operation 440, a composition value is assigned to themineral sample.

In decision operation 450, it is determined whether the compositionvalue is within a predetermined tolerance of composition values. Inreject operation 460, the assigned value of the composition is notwithin the predetermined tolerance (i.e., the characteristics do not fitwith in a pattern), and, thus, the mineral sample is diverted to a wastepile. In accept operation 470, the assigned value of the composition iswithin the predetermined tolerance (i.e., the characteristics fit withina pattern), and thus, the mineral sample is diverted to ahydrometallurgical or pyrometallurgical process.

Computer System Overview

Embodiments of the present invention include various steps andoperations, which have been described above. A variety of these stepsand operations may be performed by hardware components or may beembodied in machine-executable instructions, which may be used to causea general-purpose or special-purpose processor programmed with theinstructions to perform the steps. Alternatively, the steps may beperformed by a combination of hardware, software, and/or firmware. Assuch, FIG. 5 is an example of a computer system 500 with whichembodiments of the present invention may be utilized. According to thepresent example, the computer system includes a bus 510, at least oneprocessor 520, at least one communication port 530, a main memory 540, aremovable storage media 550, a read only memory 560, and a mass storage570.

Processor(s) 520 can be any known processor, such as, but not limitedto, an Intel® Itanium® or Itanium 2® processor(s); AMD® Opteron® orAthlon MP® processor(s); or Motorola® lines of processors. Communicationport(s) 530 can be any of an RS-232 port for use with a modem-baseddialup connection, a 10/100 Ethernet port, or a Gigabit port usingcopper or fiber. Communications may also take place over wirelessinterfaces. Communication port(s) 530 may be chosen depending on anetwork such as a Local Area Network (LAN), Wide Area Network (WAN), orany network to which the computer system 500 connects.

Main memory 540 can be Random Access Memory (RAM) or any other dynamicstorage device(s) commonly known in the art. Read only memory 560 can beany static storage device(s) such as Programmable Read Only Memory(PROM) chips for storing static information such as instructions forprocessor 520.

Mass storage 570 can be used to store information and instructions. Forexample, hard disks such as the Adaptec® family of SCSI drives, anoptical disc, an array of disks such as RAID, such as the Adaptec familyof RAID drives, or any other mass storage devices may be used.

Bus 510 communicatively couples processor(s) 520 with the other memory,storage and communication blocks. Bus 510 can be a PCI/PCI-X or SCSIbased system bus depending on the storage devices used.

Removable storage media 550 can be any kind of external hard-drives,floppy drives, IOMEGA® Zip Drives, Compact Disc-Read Only Memory(CD-ROM), Compact Disc-Re-Writable (CD-RW), and/or Digital VideoDisk-Read Only Memory (DVD-ROM).

Although not required, aspects of the invention may be practiced in thegeneral context of computer-executable instructions, such as routinesexecuted by a general-purpose data processing device, e.g., a servercomputer, wireless device or personal computer. Those skilled in therelevant art will appreciate that aspects of the invention can bepracticed with other communications, data processing, or computer systemconfigurations, including: Internet appliances, hand-held devices(including personal digital assistants (PDAs)), wearable computers, allmanner of cellular or mobile phones (including Voice over IP (VoIP)phones), dumb terminals, multi-processor systems, microprocessor-basedor programmable consumer electronics, set-top boxes, network PCs,mini-computers, mainframe computers, and the like.

Aspects of the invention can be embodied in a special purpose computeror data processor that is specifically programmed, configured, orconstructed to perform one or more of the computer-executableinstructions explained in detail herein. While aspects of the invention,such as certain functions, are described as being performed exclusivelyon a single device, the invention can also be practiced in distributedenvironments where functions or modules are shared among disparateprocessing devices, which are linked through a communications network,such as a Local Area Network (LAN), Wide Area Network (WAN), or theInternet. In a distributed computing environment, program modules may belocated in both local and remote memory storage devices.

Aspects of the invention may be stored or distributed on tangiblecomputer-readable media, including magnetically or optically readablecomputer discs, hard-wired or preprogrammed chips (e.g., EEPROMsemiconductor chips), nanotechnology memory, biological memory, or otherdata storage media. Alternatively, computer implemented instructions,data structures, screen displays, and other data under aspects of theinvention may be distributed over the Internet or over other networks(including wireless networks), on a propagated signal on a propagationmedium (e.g., an electromagnetic wave(s), a sound wave, etc.) over aperiod of time, or they may be provided on any analog or digital network(packet switched, circuit switched, or other scheme).

CONCLUSION

As one of ordinary skill in the art will appreciate based on thedetailed description provided herein, and various novel concepts arerealized, some of which are listed below:

-   -   1. A source-detector type electromagnetic sorting system        comprising:        -   a. a device for the introduction of mineral feed to the            sensor;        -   b. a device for the generation of a range of excitation            beams;        -   c. a scanner for the detection of resulting reflected,            absorbed, or backscattered energy;        -   d. an analog to digital converter to digitize both the            signal in (c);        -   e. a software program for signal analysis, data recording,            and process control;        -   f. a control system for processing signal outputs; and        -   g. a diverter connected to the control system for the            diversion of minerals.    -   2. The source-detector type electromagnetic sorting system of        claim 1, wherein the software program comprises:        -   a. a subroutine to convert incoming analog signals to            digital format        -   b. a subroutine to express spectral content of the converted            analog signal        -   c. a subroutine to perform spectral analysis on both            digitized signals in 1(c), determining frequency or            wavelength content and amplitude;        -   d. a subroutine to calibrate the system;        -   e. a subroutine to record the response data in (b) and (c)            along with additional user defined fields;        -   f. a subroutine to compare spectral response data to            previously recorded spectral data from samples of known            composition by means of conditional random field analysis;        -   g. a subroutine to generate a sort signal based on the            comparison in (f); and        -   h. a graphical user interface to control operation and data            recording.    -   3. A method of determining the spectral response of a mineral        sample under irradiation by electromagnetic means using said        system comprising:        -   a. providing said source detector sensing and sorting            system;        -   b. exposing said sensor to a mineral sample;        -   c. converting the spectral response of said mineral sample            to digital format by means of the software program in 1(e);        -   d. measuring the spectral response of said mineral sample to            said sensor by means of the software program in 1(e); and        -   e. converting the measured response (c) into a power            spectrum by means of the software program described in 1(e).        -   f. assigning an appropriate threshold of acceptance for            spectral responses above a certain pre-determined value and            ‘training’ the algorithm to recognize those responses    -   4. A method of determining the mineral composition of an unknown        sample using said sensor comprising:        -   a. providing said system;        -   b. measuring the spectral response due to the unknown            sample;        -   c. using the software program described in 1(e) to compare            the measured data in (b) to previously recorded response            data from samples of known grade as describe in 3; and        -   d. using said software program to assign a compositional            value to the unknown sample based on the comparison in (c).    -   5. A method of discriminating mineral samples based on spectral        response using said sensor comprising:        -   a. providing said system;        -   b. determining characteristic spectral response of the            mineral sample as described in 3 and 4;        -   c. using the software program in 1(e) to compare the values            determined in (b) to predefined spectra of previously            characterized mineral samples by means of the conditional            random field algorithm described; and        -   d. using the control system described in 1(f) to control the            diverter system based upon results of the comparison            described in (c).    -   6. A method of automatically rejecting or accepting mineral        samples based on spectral response using the system of claim 1        comprising the steps of:        -   a. providing said system;        -   b. discriminating between sample materials;        -   c. using the software program in 1(e) to generate a sort            decision based on the discrimination in (b); and        -   d. effecting the sort based on the decision in (c) by means            of the sorting mechanism described in 1 and 2.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof means any connection or coupling,either direct or indirect, between two or more elements; the coupling orconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this application, refer tothis application as a whole and not to any particular portions of thisapplication. Where the context permits, words in the above DetailedDescription using the singular or plural number may also include theplural or singular number respectively. The word “or,” in reference to alist of two or more items, covers all of the following interpretationsof the word: any of the items in the list, all of the items in the list,and any combination of the items in the list.

The above Detailed Description of examples of the invention is notintended to be exhaustive or to limit the invention to the precise formdisclosed above. While specific examples for the invention are describedabove for illustrative purposes, various equivalent modifications arepossible within the scope of the invention, as those skilled in therelevant art will recognize. For example, while processes or blocks arepresented in a given order, alternative implementations may performroutines having steps, or employ systems having blocks, in a differentorder, and some processes or blocks may be deleted, moved, added,subdivided, combined, and/or modified to provide alternative orsubcombinations. Each of these processes or blocks may be implemented ina variety of different ways. Also, while processes or blocks are attimes shown as being performed in series, these processes or blocks mayinstead be performed or implemented in parallel, or may be performed atdifferent times. Further any specific numbers noted herein are onlyexamples: alternative implementations may employ differing values orranges.

The teachings of the invention provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various examples described above can be combined to providefurther implementations of the invention. Some alternativeimplementations of the invention may include not only additionalelements to those implementations noted above, but also may includefewer elements. Any patents and applications and other references notedabove, including any that may be listed in accompanying filing papers,are incorporated herein by reference. Aspects of the invention can bemodified, if necessary, to employ the systems, functions, and conceptsof the various references described above to provide yet furtherimplementations of the invention.

These and other changes can be made to the invention in light of theabove Detailed Description. While the above description describescertain examples of the invention, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the invention disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the invention under theclaims.

To reduce the number of claims, certain embodiments of the invention arepresented below in certain claim forms, but the applicant contemplatesthe various aspects of the invention in any number of claim forms. Forexample, while only one aspect of the invention is recited as ameans-plus-function claim under 35 U.S.C. sec. 112, sixth paragraph,other aspects may likewise be embodied as a means-plus-function claim,or in other forms, such as being embodied in a computer-readable medium.(Any claims intended to be treated under 35 U.S.C. §112, ¶6 will beginwith the words “means for”, but use of the term “for” in any othercontext is not intended to invoke treatment under 35 U.S.C. §112, ¶6.)Accordingly, the applicant reserves the right to pursue additionalclaims after filing this application to pursue such additional claimforms, in either this application or in a continuing application.

As one of ordinary skill in the art will appreciate based on thedetailed description provided herein, and various novel concepts arerealized. The Abstract of the Disclosure is provided to comply with 37C.F.R. section 1.72(b), requiring an abstract that will allow the readerto quickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments of the inventionrequire more features than are expressly recited in each claim. Rather,as the following claims reflect, inventive subject matter lies in lessthan all features of a single disclosed embodiment. Thus the followingclaims are hereby incorporated into the Detailed Description, with eachclaim standing on its own as a separate preferred embodiment:

We claim:
 1. A method of sorting a stream of minerals, comprising:receiving response data indicating reflected, absorbed or backscatteredenergy from a mineral sample exposed to a sensor, wherein the mineralsample is irradiated with electromagnetic energy; determining spectralcharacteristics of the mineral sample by performing spectral analysis onthe response data of the mineral sample; identifying a composition ofthe mineral sample by comparing the spectral characteristics of themineral sample to previously developed spectral characteristics ofsamples of known composition; and generating a sort decision for themineral sample based on the comparison, wherein the sort decision isused in diverting the mineral sample to a desired destination.
 2. Themethod of claim 1 wherein the spectral characteristics include at leastone of frequency content, wavelength content, or amplitude of theresponse data.
 3. The method of claim 1 further comprising collectingspectral response data from a series of mineral samples irradiated withelectromagnetic energy and exposed to a sensor to develop the spectralcharacteristics of samples of known composition.
 4. The method of claim1 wherein the spectral characteristics of samples of known compositionare developed using at least one of: conditional random fieldalgorithms, Bayesian network, one or more Markov models, knowledge-basedtechniques, neural networks, or fuzzy logic techniques.
 5. The method ofclaim 1 wherein identifying a composition of the mineral sample includesassigning a compositional value to the mineral sample based on thecomparison.
 6. The method of claim 5 wherein the mineral sample isdiverted to an accept pile when the compositional value is within apredetermined variation of the previously developed spectralcharacteristics.
 7. The method of claim 1 wherein comparing the spectralcharacteristics of the mineral sample to previously developed spectralcharacteristics of samples of known composition includes matching thespectral characteristics of the mineral sample to previously developedpattern sets.
 8. At least one tangible computer-readable medium carryinginstructions, which when executed by at least one processor, determinesa composition of a mineral sample, comprising: measuring a spectralresponse of a mineral sample to electromagnetic radiation; comparing themeasured spectral response to previously recorded response data fromsamples of known composition to identify a composition of the mineralsample; and assigning a compositional value to the mineral sample basedon the comparison.
 9. The at least one tangible computer-readable mediumof claim 8, wherein the instructions, which when executed by the atleast one processor, further comprise: generating a signal based on thecompositional value.
 10. The at least one tangible computer-readablemedium of claim 9, wherein the signal is a sort signal used to divertthe mineral sample to a desired location.
 11. The at least one tangiblecomputer-readable medium of claim 8, wherein the mineral sample is alaterite, and wherein the sort signal is used to divert the mineralsample to one of: a hydrometallurgical process, a pyrometallurgicalprocess, or a waste pile.
 12. The at least one tangiblecomputer-readable medium of claim 8, wherein the instructions, whichwhen executed by the at least one processor, further comprise:transporting the sample into a source field.
 13. A system for sortingminerals, comprising: a device to introduce mineral samples to a sensor;a device to generate a range of excitation beams to apply to the mineralsamples; a scanner to detect response data including resulting reflectedor absorbed energy from the mineral samples; a means for comparing theresponse data to previously determined response data of samples of knowncomposition to determine a composition of the mineral samples.
 14. Thesystem of claim 13 further comprising: an analog to digital converter todigitize the detected response data; a control system to process signaloutputs; and a diverter coupled to the control system for the diversionof the mineral samples.
 15. The system of claim 13 further comprising agraphical user interface to control operation and record data.
 16. Thesystem of claim 13 wherein the response data of samples of knowncomposition includes spectral characteristics.
 17. The system of claim16 wherein the spectral characteristics of samples of known compositionare developed using at least one of: conditional random fieldalgorithms, Bayesian network, one or more Markov models, knowledge-basedtechniques, neural networks, or fuzzy logic techniques.
 18. A method ofmaximizing the recovered value of mineral ores, comprising: extractingunblended mineral ore from a mine bench or pit using a mechanicalexcavator or similar earthmoving device; delivering the unblendedmineral ore to a haul truck or conveyor belt using the excavator;transporting the unblended mineral ore to a mineral sorting system, themineral sorting system configured to: apply electromagnetic radiation tothe mineral ore, detect a response of the mineral ore to theelectromagnetic radiation, and compare the response data to previouslydetermined response data of samples of known composition to determinethe composition of the mineral ore; and classifying the mineral ore,based on the comparison, into a first mineral product, a second mineralproduct, and a third mineral product.
 19. The method of claim 18,wherein: the first mineral product is conditioned for pyrometallurgicaltreatment, the second mineral product is conditioned forhydrometallurgical treatment, and the third mineral product isconditioned for disposal as a waste product.
 20. The method of claim 18,wherein the first mineral product, the second mineral product and thethird mineral product are simultaneously classified by the mineralsorting system.